LEARNING METHOD AND LEARNING DEVICE FOR CNN USING 1xK OR Kx1 CONVOLUTION TO BE USED FOR HARDWARE OPTIMIZATION, AND TESTING METHOD AND TESTING DEVICE USING THE SAME

ABSTRACT

A method for learning parameters of a CNN using a 1×K convolution operation or a K×1 convolution operation is provided to be used for hardware optimization which satisfies KPI. The method includes steps of: a learning device (a) instructing a reshaping layer to two-dimensionally concatenate features in each group comprised of corresponding K channels of a training image or its processed feature map, to thereby generate a reshaped feature map, and instructing a subsequent convolutional layer to apply the 1×K or the K×1 convolution operation to the reshaped feature map, to thereby generate an adjusted feature map; and (b) instructing an output layer to refer to features on the adjusted feature map or its processed feature map, and instructing a loss layer to calculate losses by referring to an output from the output layer and its corresponding GT.

FIELD OF THE DISCLOSURE

The present disclosure relates to a method for learning parameters of aCNN using a 1×K convolution operation or a K×1 convolution operation tobe used for hardware optimization; and more particularly, to the methodfor learning the parameters of the CNN using the 1×K convolutionoperation or the K×1 convolution operation, including steps of: (a) ifat least one training image is acquired, instructing a reshaping layerto two-dimensionally concatenate each of features in each groupcomprised of each corresponding K channels among all channels of thetraining image or its processed feature map, to thereby generate areshaped feature map, and instructing a subsequent convolutional layerto apply the 1×K convolution operation or the K×1 convolution operationto the reshaped feature map, to thereby generate an adjusted feature mapwhose volume is adjusted; and (b) instructing an output layer togenerate at least one output by referring to features on the adjustedfeature map or its processed feature map, and instructing a loss layerto calculate one or more losses by referring to the output and itscorresponding at least one ground truth, to thereby learn at least partof parameters of the subsequent convolutional layer by backpropagatingthe losses, and a learning device, a testing method, and a testingdevice using the same.

BACKGROUND OF THE DISCLOSURE

Deep Convolution Neural Networks (Deep CNNs) are at the heart of theremarkable development in deep learning. CNNs have already been used inthe 90's to solve the problems of character recognition, but their usehas become as widespread as it is now thanks to recent research. Thesedeep CNNs won the 2012 ImageNet image classification tournament,crushing other competitors. Then, the convolutional neural networkbecame a very useful tool in the field of the machine learning.

The CNN may include a feature extractor which extracts features from animage, and a feature classifier which detects objects in the image orrecognizes the objects in the image by referring to the featuresextracted by the feature extractor.

The feature extractor of the CNN may include convolutional layers, andthe feature classifier may include one or more FC layers capable ofapplying fully connected operations to the features extracted by thefeature extractor.

The convolutional layers are the most important part of the CNN which doalmost all of the operation.

The convolutional layers extract features from a local receptive fieldby further using information on pixels nearby. However, a characteristicof filters of the convolutional layers are linear, thus the filterscannot extract non-linear features very well. This problem can beresolved by increasing the number of feature maps, but then the amountof computation also increases.

As such, a 1×1 convolutional layer has been used to reduce the dimensionof the feature maps to minimize computational load.

The 1×1 convolutional layer can extract like features from multiplefeature maps, and as a result, can reduce the number of the featuremaps, thus can reduce the amount of computation.

Also, the reduction of the amount of computation gives room fordeepening the neural network.

Also, the 1×1 convolutional layer is used for image segmentation, orused in place of the FC layers for the feature extractor.

Herein, the inventors of the present disclosure propose a CNN capable ofreducing the amount of the convolution operation more effectively,compared to convolution operation of the 1×1 convolutional layer.

SUMMARY OF THE DISCLOSURE

It is an object of the present disclosure to solve all theaforementioned problems.

It is another object of the present disclosure to provide a CNN capableof reducing amount of convolution operation more efficiently.

It is still another object of the present disclosure to provide the CNNcapable of extracting features on an image more efficiently by theconvolution operation.

In accordance with one aspect of the present disclosure, there isprovided a method for learning parameters of a CNN using a 1×Kconvolution operation or a K×1 convolution operation, including stepsof: (a) a learning device, if at least one training image is acquired,instructing a reshaping layer to two-dimensionally concatenate each offeatures in each group comprised of each corresponding K channels amongall channels of the training image or its processed feature map, tothereby generate a reshaped feature map, and instructing a subsequentconvolutional layer to apply the 1×K convolution operation or the K×1convolution operation to the reshaped feature map, to thereby generatean adjusted feature map whose volume is adjusted; and (b) the learningdevice instructing an output layer to generate at least one output byreferring to features on the adjusted feature map or its processedfeature map, and instructing a loss layer to calculate one or morelosses by referring to the output and its corresponding at least oneground truth, to thereby learn at least part of parameters of thesubsequent convolutional layer by backpropagating the losses.

As one example, at the step of (a), the learning device, if the numberof channels of the training image or its processed feature map is not amultiple of K, instructs the reshaping layer to add at least one dummychannel to the channels of the training image or its processed featuremap such that the number of the channels including the at least onedummy channel is a multiple of K, and to concatenate said each offeatures in said each group comprised of said each corresponding Kchannels among said all channels, including the at least one dummychannel, of the training image or its processed feature map.

As one example, supposing that a width of the training image or itsprocessed feature map is W and a height thereof is H, and that thenumber of channels thereof is L, at the step of (a), the learning deviceinstructs the reshaping layer to generate the reshaped feature maphaving a width of W, a height of H·K, and a channel of

${CEIL}{\left( \frac{L}{K} \right).}$

As one example, supposing that the number of kernels of the subsequentconvolutional layer is M, at the step of (a), the learning deviceinstructs the subsequent convolutional layer to apply a 1×K convolutionoperation to the reshaped feature map, to thereby generate the adjustedfeature map having a volume of W·H·M, resulting from a width of W, aheight of H, and a channel of M.

As one example, supposing that a width of the training image or itsprocessed feature map is W and a height thereof is H, and that thenumber of channels thereof is L, at the step of (a), the learning deviceinstructs the reshaping layer to generate the reshaped feature maphaving a width of W·K, a height of H, and a channel of

${CEIL}{\left( \frac{L}{K} \right).}$

As one example, supposing that the number of kernels of the subsequentconvolutional layer is M, at the step of (a), the learning deviceinstructs the subsequent convolutional layer to apply a K×1 convolutionoperation to the reshaped feature map, to thereby generate the adjustedfeature map having a volume of W·H·M, resulting from a width of W, aheight of H, and a channel of M.

As one example, supposing that a width of the training image or itsprocessed feature map is W and a height thereof is H, and that thenumber of channels thereof is L, at the step of (a), the learning deviceinstructs the reshaping layer to (i) generate the reshaped feature maphaving a width of W, a height of H·K, and a channel of

${{CEIL}\left( \frac{L}{K} \right)},$

or (ii) generate the reshaped feature map having a width of W·K, aheight of H, and a channel of

${{CEIL}\left( \frac{L}{K} \right)},$

and, if a size of a final part of the reshaped feature map on a

$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} \text{-}{th}$

channel is different from a size of a width of W and a height of H·K,the learning device instructs the reshaping layer to add at least onezero padding such that the final part of the reshaped feature map on the

$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} \text{-}{th}$

channel has the width of W and the height of H·K, or wherein, if thesize of the final part of the reshaped feature map on the

$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} \text{-}{th}$

channel is different from a size of a width of W·K and a height of H,the learning device instructs the reshaping layer to add at least onezero padding such that the final part of the reshaped feature map on the

$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} \text{-}{th}$

channel has the width of W·K and the height of H.

In accordance with another aspect of the present disclosure, there isprovided a method for testing a CNN using a 1×K convolution operation ora K×1 convolution operation, including steps of: (a) on condition that alearning device (i) has instructed a reshaping layer totwo-dimensionally concatenate each of features for training in eachgroup comprised of each corresponding K channels among all channels ofat least one training image or its processed feature map, to therebygenerate a reshaped feature map for training, and has instructed asubsequent convolutional layer to apply the 1×K convolution operation orthe K×1 convolution operation to the reshaped feature map for training,to thereby generate an adjusted feature map for training whose volume isadjusted, and (ii) has instructed an output layer to generate at leastone output for training by referring to features on the adjusted featuremap for training or its processed feature map, and has instructed a losslayer to calculate one or more losses by referring to the output fortraining and its corresponding at least one ground truth, to therebylearn at least part of parameters of the subsequent convolutional layerby backpropagating the losses; a testing device, if at least one testimage is acquired, instructing the reshaping layer to two-dimensionallyconcatenate each of features for testing in each group comprised of eachcorresponding K channels among all channels of the test image or itsprocessed feature map, to thereby generate a reshaped feature map fortesting, and instructing the subsequent convolutional layer to apply the1×K convolution operation or the K×1 convolution operation to thereshaped feature map for testing, to thereby generate an adjustedfeature map for testing whose volume is adjusted; and (b) the testingdevice instructing the output layer to generate at least one output fortesting by referring to features on the adjusted feature map for testingor its processed feature map.

As one example, at the step of (a), the testing device, if the number ofchannels of the test image or its processed feature map is not amultiple of K, instructs the reshaping layer to add at least one dummychannel to the channels of the test image or its processed feature mapsuch that the number of the channels including the at least one dummychannel is a multiple of K, and to concatenate said each of features insaid each group comprised of said each corresponding K channels amongsaid all channels, including the at least one dummy channel, of the testimage or its processed feature map.

As one example, supposing that a width of the test image or itsprocessed feature map is W and a height thereof is H, and that thenumber of channels thereof is L, at the step of (a), the testing deviceinstructs the reshaping layer to generate the reshaped feature map fortesting having a width of W, a height of H·K, and a channel of

${CEIL}{\left( \frac{L}{K} \right).}$

As one example, supposing that the number of kernels of the subsequentconvolutional layer is M, at the step of (a), the testing deviceinstructs the subsequent convolutional layer to apply a 1×K convolutionoperation to the reshaped feature map for testing, to thereby generatethe adjusted feature map for testing having a volume of W·H·M, resultingfrom a width of W, a height of H, and a channel of M.

As one example, supposing that a width of the test image or itsprocessed feature map is W and a height thereof is H, and that thenumber of channels thereof is L, at the step of (a), the testing deviceinstructs the reshaping layer to generate the reshaped feature map fortesting having a width of W·K, a height of H, and a channel of

${CEIL}{\left( \frac{L}{K} \right).}$

As one example, supposing that the number of kernels of the subsequentconvolutional layer is M, at the step of (a), the testing deviceinstructs the subsequent convolutional layer to apply a K×1 convolutionoperation to the reshaped feature map for testing, to thereby generatethe adjusted feature map for testing having a volume of W·H·M, resultingfrom a width of W, a height of H, and a channel of M.

As one example, supposing that a width of the test image or itsprocessed feature map is W and a height thereof is H, and that thenumber of channels thereof is L, at the step of (a), the testing deviceinstructs the reshaping layer to (i) generate the reshaped feature mapfor testing having a width of W, a height of H·K, and a channel of

${{CEIL}\left( \frac{L}{K} \right)},$

or (ii) generate the reshaped feature map for testing having a width ofW·K, a height of H, and a channel of

${{CEIL}\ \left( \frac{L}{K} \right)},$

and, if a size of a final part of the reshaped feature map for testingon a

$\left\{ {{CEIL}\ \left( \frac{L}{K} \right)} \right\} - {th}$

channel is different from a size of a width of W and a height of H·K,the testing device instructs the reshaping layer to add at least onezero padding such that the final part of the reshaped feature map fortesting on the

$\left\{ {{CEIL}\ \left( \frac{L}{K} \right)} \right\} - {th}$

channel has the width of W and the height of H·K, or wherein, if thesize of the final part of the reshaped feature map for testing on the

$\left\{ {{CEIL}\ \left( \frac{L}{K} \right)} \right\} - {th}$

channel is different from a size of a width of W·K and a height of H,the testing device instructs the reshaping layer to add at least onezero padding such that the final part of the reshaped feature map fortesting on the

$\left\{ {{CEIL}\ \left( \frac{L}{K} \right)} \right\} - {th}$

channel has the width of W·K and the height of H.

In accordance with still another aspect of the present disclosure, thereis provided a learning device for learning parameters of a CNN using a1×K convolution operation or a K×1 convolution operation, including: atleast one memory that stores instructions; and at least one processorconfigured to execute the instructions to: perform processes of (I)instructing a reshaping layer to two-dimensionally concatenate each offeatures in each group comprised of each corresponding K channels amongall channels of at least one training image or its a processed featuremap, to thereby generate a reshaped feature map, and instructing asubsequent convolutional layer to apply the 1×K convolution operation orthe K×1 convolution operation to the reshaped feature map, to therebygenerate an adjusted feature map whose volume is adjusted, and (II)instructing an output layer to generate at least one output by referringto features on the adjusted feature map or its processed feature map,and instructing a loss layer to calculate one or more losses byreferring to the output and its corresponding at least one ground truth,to thereby learn at least part of parameters of the subsequentconvolutional layer by backpropagating the losses.

As one example, at the process of (I), the processor, if the number ofchannels of the training image or its the pre-processed feature map isnot a multiple of K, instructs the reshaping layer to add at least onedummy channel to the channels of the training image or its processedfeature map such that the number of the channels including the at leastone dummy channel is a multiple of K, and to concatenate said each offeatures in said each group comprised of said each corresponding Kchannels among said all channels, including the at least one dummychannel, of the training image or its the pre-processed feature map.

As one example, supposing that a width of the training image or itspre-processed feature map is W and a height thereof is H, and that thenumber of channels thereof is L, at the process of (I), the processorinstructs the reshaping layer to generate the reshaped feature maphaving a width of W, a height of H·K, and a channel of

${{CEIL}\left( \frac{L}{K} \right)}.$

As one example, supposing that the number of kernels of the subsequentconvolutional layer is M, at the process of (I), the processor instructsthe subsequent convolutional layer to apply a 1×K convolution operationto the reshaped feature map, to thereby generate the adjusted featuremap having a volume of W·H·M, resulting from a width of W, a height ofH, and a channel of M.

As one example, supposing that a width of the training image or its thepre-processed feature map is W and a height thereof is H, and that thenumber of channels thereof is L, at the process of (I), the processorinstructs the reshaping layer to generate the reshaped feature maphaving a width of W·K, a height of H, and a channel of

${{CEIL}\left( \frac{L}{K} \right)}.$

As one example, supposing that the number of kernels of the subsequentconvolutional layer is M, at the process of (I), the processor instructsthe subsequent convolutional layer to apply a K×1 convolution operationto the reshaped feature map, to thereby generate the adjusted featuremap having a volume of W·H·M, resulting from a width of W, a height ofH, and a channel of M.

As one example, supposing that a width of the training image or its thepre-processed feature map is W and a height thereof is H, and that thenumber of channels thereof is L, at the process of (I), the processorinstructs the reshaping layer to (i) generate the reshaped feature maphaving a width of W, a height of H·K, and a channel of

${{CEIL}\left( \frac{L}{K} \right)},$

or (ii) generate the reshaped feature map having a width of W·K, aheight of H, and a channel of

${{CEIL}\left( \frac{L}{K} \right)},$

and, if a size of a final part of the reshaped feature map on a

$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} \text{-}{th}$

channel is different from a size of a width of W and a height of H·K,the processor instructs the reshaping layer to add at least one zeropadding such that the final part of the reshaped feature map on the

$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} \text{-}{th}$

channel has the width of W and the height of H·K, or wherein, if thesize of the final part of the reshaped feature map on the

$\left\{ {{CEIL}\ \left( \frac{L}{K} \right)} \right\} - {th}$

channel is different from a size of a width of W·K and a height of H,the processor instructs the reshaping layer to add at least one zeropadding such that the final part of the reshaped feature map on the

$\left\{ {{CEIL}\ \left( \frac{L}{K} \right)} \right\} - {th}$

channel has the width of W·K and the height of H.

In accordance with still yet another aspect of the present disclosure,there is provided a testing device for testing a CNN using a 1×Kconvolution operation or a K×1 convolution operation, including: atleast one memory that stores instructions; and at least one processor,on condition that a learning device (i) has instructed a reshaping layerto two-dimensionally concatenate each of features for training in eachgroup comprised of each corresponding K channels among all channels ofat least one training image or its processed feature map, to therebygenerate a reshaped feature map for training, and has instructed asubsequent convolutional layer to apply the 1×K convolution operation orthe K×1 convolution operation to the reshaped feature map for training,to thereby generate an adjusted feature map for training whose volume isadjusted, and (ii) has instructed an output layer to generate at leastone output for training by referring to features on the adjusted featuremap for training or its processed feature map, and has instructed a losslayer to calculate one or more losses by referring to the output fortraining and its corresponding at least one ground truth, to therebylearn at least part of parameters of the subsequent convolutional layerby backpropagating the losses; configured to execute the instructionsto: perform processes of (I) instructing the reshaping layer totwo-dimensionally concatenate each of features for testing in each groupcomprised of each corresponding K channels among all channels of atleast one test image or its processed feature map, to thereby generate areshaped feature map for testing, and instructing the subsequentconvolutional layer to apply the 1×K convolution operation or the K×1convolution operation to the reshaped feature map for testing, tothereby generate an adjusted feature map for testing whose volume isadjusted, and (II) instructing the output layer to generate at least oneoutput for testing by referring to features on the adjusted feature mapfor testing or its processed feature map.

As one example, at the process of (I), the processor, if the number ofchannels of the test image or its processed feature map is not amultiple of K, instructs the reshaping layer to add at least one dummychannel to the channels of the test image or its processed feature mapsuch that the number of the channels including the at least one dummychannel is a multiple of K, and to concatenate said each of features insaid each group comprised of said each corresponding K channels amongsaid all channels, including the at least one dummy channel, of the testimage or its processed feature map.

As one example, supposing that a width of the test image or itsprocessed feature map is W and a height thereof is H, and that thenumber of channels thereof is L, at the process of (I), the processorinstructs the reshaping layer to generate the reshaped feature map fortesting having a width of W, a height of H·K, and a channel of

${{CEIL}\ \left( \frac{L}{K} \right)}.$

As one example, supposing that the number of kernels of the subsequentconvolutional layer is M, at the process of (I), the processor instructsthe subsequent convolutional layer to apply a 1×K convolution operationto the reshaped feature map for testing, to thereby generate theadjusted feature map for testing having a volume of W·H·M, resultingfrom a width of W, a height of H, and a channel of M.

As one example, supposing that a width of the test image or itsprocessed feature map is W and a height thereof is H, and that thenumber of channels thereof is L, at the process of (I), the processorinstructs the reshaping layer to generate the reshaped feature map fortesting having a width of W·K, a height of H, and a channel of

${{CEIL}\ \left( \frac{L}{K} \right)}.$

As one example, supposing that the number of kernels of the subsequentconvolutional layer is M, at the process of (I), the processor instructsthe subsequent convolutional layer to apply a K×1 convolution operationto the reshaped feature map for testing, to thereby generate theadjusted feature map for testing having a volume of W·H·M, resultingfrom a width of W, a height of H, and a channel of M.

As one example, supposing that a width of the test image or itsprocessed feature map is W and a height thereof is H, and that thenumber of channels thereof is L, at the process of (I), the processorinstructs the reshaping layer to (i) generate the reshaped feature mapfor testing having a width of W, a height of H·K, and a channel of

${{CEIL}\left( \frac{L}{K} \right)},$

or (ii) generate the reshaped feature map for testing having a width ofW·K, a height of H, and a channel of

${{CEIL}\left( \frac{L}{K} \right)},$

and, if a size of a final part of the reshaped feature map for testingon a

$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} \text{-}{th}$

channel is different from a size of a width of W and a height of H·K,the processor instructs the reshaping layer to add at least one zeropadding such that the final part of the reshaped feature map for testingon the

$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} \text{-}{th}$

channel has the width of W and the height of H·K, or wherein, if thesize of the final part of the reshaped feature map for testing on the

$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} \text{-}{th}$

channel is different from a size of a width of W·K and a height of H,the processor instructs the reshaping layer to add at least one zeropadding such that the final part of the reshaped feature map for testingon the

$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} \text{-}{th}$

channel has the width of W·K and the height of H.

In addition, recordable media that are readable by a computer forstoring a computer program to execute the method of the presentdisclosure is further provided.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects and features of the present disclosure willbecome apparent from the following description of preferred embodimentsgiven in conjunction with the accompanying drawings.

The following drawings to be used to explain example embodiments of thepresent disclosure are only part of example embodiments of the presentdisclosure and other drawings can be obtained based on the drawings bythose skilled in the art of the present disclosure without inventivework.

FIG. 1 is a drawing schematically illustrating a learning device forlearning a CNN using a 1×K or a K×1 convolution operation in accordancewith one example embodiment of the present disclosure.

FIG. 2 is a drawing schematically illustrating a learning method forlearning the CNN using the 1×K or the K×1 convolution operation inaccordance with one example embodiment of the present disclosure.

FIG. 3 is a drawing schematically illustrating a training image or itsprocessed feature map for learning the CNN using the 1×K or the K×1convolution operation in accordance with one example embodiment of thepresent disclosure.

FIGS. 4A and 4B are drawings schematically illustrating (i) a reshapedfeature map generated from reshaping the training image or its processedfeature map and (ii) an adjusted feature map generated from applying the1×K convolution operation to the reshaped feature map, respectively, inaccordance with one example embodiment of the present disclosure.

FIGS. 5A and 5B are drawings schematically illustrating (i) a reshapedfeature map generated from reshaping the training image or its processedfeature map and (ii) an adjusted feature map generated from applying theK×1 convolution operation to the reshaped feature map, respectively, inaccordance with one example embodiment of the present disclosure.

FIG. 6 is a drawing schematically illustrating a testing device for theCNN using the 1×K or the K×1 convolution operation in accordance withone example embodiment of the present disclosure.

FIG. 7 is a drawing schematically illustrating a testing method for theCNN using the 1×K or the K×1 convolution operation in accordance withone example embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Detailed explanation on the present disclosure to be made below refer toattached drawings and diagrams illustrated as specific embodimentexamples under which the present disclosure may be implemented to makeclear of purposes, technical solutions, and advantages of the presentdisclosure. These embodiments are described in sufficient detail toenable those skilled in the art to practice the disclosure.

Besides, in the detailed description and claims of the presentdisclosure, a term “include” and its variations are not intended toexclude other technical features, additions, components or steps. Otherobjects, benefits, and features of the present disclosure will berevealed to one skilled in the art, partially from the specification andpartially from the implementation of the present disclosure. Thefollowing examples and drawings will be provided as examples but theyare not intended to limit the present disclosure.

Moreover, the present disclosure covers all possible combinations ofexample embodiments indicated in this specification. It is to beunderstood that the various embodiments of the present disclosure,although different, are not necessarily mutually exclusive. For example,a particular feature, structure, or characteristic described herein inconnection with one embodiment may be implemented within otherembodiments without departing from the spirit and scope of the presentdisclosure. In addition, it is to be understood that the position orarrangement of individual elements within each disclosed embodiment maybe modified without departing from the spirit and scope of the presentdisclosure. The following detailed description is, therefore, not to betaken in a limiting sense, and the scope of the present disclosure isdefined only by the appended claims, appropriately interpreted, alongwith the full range of equivalents to which the claims are entitled. Inthe drawings, like numerals refer to the same or similar functionalitythroughout the several views.

Any images referred to in the present disclosure may include imagesrelated to any roads paved or unpaved, in which case the objects on theroads or near the roads may include vehicles, persons, animals, plants,buildings, flying objects like planes or drones, or any other obstacleswhich may appear in a road-related scene, but the scope of the presentdisclosure is not limited thereto. As another example, said any imagesreferred to in the present disclosure may include images not related toany roads, such as images related to alleyway, land lots, sea, lakes,rivers, mountains, forests, deserts, sky, or any indoor space, in whichcase the objects in said any images may include vehicles, persons,animals, plants, buildings, flying objects like planes or drones, ships,amphibious planes or ships, or any other obstacles which may appear in ascene related to alleyway, land lots, sea, lakes, rivers, mountains,forests, deserts, sky, or any indoor space, but the scope of the presentdisclosure is not limited thereto.

To allow those skilled in the art to the present disclosure to becarried out easily, the example embodiments of the present disclosure byreferring to attached diagrams will be explained in detail as shownbelow.

FIG. 1 is a drawing schematically illustrating a learning device of aCNN using a 1×K or a K×1 convolution operation in accordance with oneexample embodiment of the present disclosure, and by referring to FIG.1, the learning device 100 may include a communication part 110 and aprocessor 120.

First, the communication part 110 may receive at least one trainingimage.

Herein, the training image may be stored in a database 130, and thedatabase 130 may store at least one ground truth of class information oneach of one or more objects and at least one ground truth of locationinformation on each of the objects, corresponding to the trainingimages.

In addition, the learning device may further include a memory 115capable of storing computer readable instructions for performingfollowing processes. As one example, the processor, the memory, amedium, etc. may be integrated with an integrated processor.

Next, the processor 120 may perform processes of instructing a reshapinglayer to two-dimensionally concatenate each of features in each groupcomprised of each corresponding K channels among all channels of thetraining image or its processed feature map, to thereby generate areshaped feature map, and instructing a subsequent convolutional layerto apply the 1×K convolution operation or the K×1 convolution operationto the reshaped feature map, to thereby generate an adjusted feature mapwhose volume is adjusted. Herein, said processed feature map is afeature map generated by at least one of (i) a method of applying one ormore convolution operations to the training image, (ii) that of applyingsubsequent operations, e.g., a batch normalization operation, anactivation operation, a pooling operation, to a result of said method of(i), and (iii) that of applying additional convolution operations to aresult of said method of (ii). Then, the processor 120 may instruct anoutput layer to generate at least one output by referring to features onthe adjusted feature map or its processed feature map, and may instructa loss layer to calculate one or more losses by referring to the outputand its corresponding at least one ground truth, to thereby learn atleast part of parameters of the subsequent convolutional layer bybackpropagating the losses.

Herein, the learning device 100 in accordance with one example of thepresent disclosure may be a computing device and may be any digitaldevice with a processor capable of computation. For reference, althoughFIG. 1 shows the single learning device 100, the scope of the presentdisclosure is not limited thereto. For example, the learning device maybe configured as several devices to perform its functions.

A method for learning parameters of the CNN using the 1×K or the K×1convolution operation by using the learning device 100 in accordancewith one example embodiment of the present disclosure is described byreferring to FIG. 2 as follows.

First of all, if the training image is inputted, the learning device 100may instruct a pre-processing layer 121 to pre-process the trainingimage, to thereby generate the processed feature map.

Herein, the pre-processing layer 121 may include at least one of aconvolutional layer, a batch normalization layer, an activation layer,and a pooling layer, and may generate the processed feature map. Herein,the processed feature map is a feature map generated by at least one of(i) a method of applying the convolution operations to the trainingimage, (ii) that of applying subsequent operations, e.g., the batchnormalization operation, the activation operation, the poolingoperation, to a result of said method of (i), and (iii) that of applyingadditional convolution operations to a result of said method of (ii).However, the scope of the pre-processing layer 121 is not limitedthereto, that is, the pre-processing layer 121 may include each layerwhich forms the CNN for image processing.

Next, the learning device 100 may instruct a reshaping layer 122 totwo-dimensionally concatenate each of features in each group comprisedof each corresponding K channels among all channels of the trainingimage or its processed feature map, to thereby generate the reshapedfeature map. Herein, the learning device 100 may directly input thetraining image into the reshaping layer 122, without instructing thepre-processing layer 121 to pre-process the training image.

Herein, if the number of channels of the training image or its processedfeature map is not a multiple of K, the learning device 100 may instructthe reshaping layer 122 to add at least one dummy channel to thechannels corresponding to each of the pixels such that the number of thechannels including the at least one dummy channel is a multiple of K,and to concatenate said each of features in said each group comprised ofsaid each corresponding K channels among said all channels, includingthe at least one dummy channel, of the training image or its processedfeature map.

That is, supposing that a width of the training image or its processedfeature map is W and a height thereof is H, and that the number ofchannels thereof is L, the learning device 100 may instruct thereshaping layer 122 to (i) generate the reshaped feature map having awidth of W, a height of H·K, and a channel of

${{CEIL}\ \left( \frac{L}{K} \right)},$

or (ii) generate the reshaped feature map having a width of W·K, aheight of H, and a channel of

${{CEIL}\ \left( \frac{L}{K} \right)}.$

Further, if a size of a final part of the reshaped feature map on a

$\left\{ {{CEIL}\ \left( \frac{L}{K} \right)} \right\} - {th}$

channel is different from the size of the width of W and the height ofH·K, the learning device 100 may instruct the reshaping layer 122 to addat least one zero padding such that the final part of the reshapedfeature map on the

$\left\{ {{CEIL}\ \left( \frac{L}{K} \right)} \right\} - {th}$

channel has the width of W and the height of H·K, or if the size of thefinal part of the reshaped feature map on the

$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} - {th}$

channel is different from a size of a width of W·K and a height of H,the learning device 100 may instruct the reshaping layer 122 to add atleast one zero padding such that the final part of the reshaped featuremap on the

$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} - {th}$

channel has the width of W·K and the height of H.

As one example, by referring to FIG. 3, supposing that a width of thetraining image or its processed feature map 300 is W and a heightthereof is H, and that the number of channels thereof is L, each offeatures, corresponding to said each of pixels, on a first channel C1 ofthe training image or its processed feature map 300 may be representedby C1F11, . . . , C1F22, . . . , and C1FWH. Also, each of features,corresponding to said each of pixels, on other channels C2, C3, . . . ,and CL of the training image or its processed feature map 300 may berepresented in a similar way.

Then, by referring to FIGS. 3 and 4A, features C1F11, C2F11, . . . , andCKF11 of K channels corresponding to the pixel C1F11 on the trainingimage or its processed feature map 300 in FIG. 3 may be concatenated,features C1F21, C2F21, . . . , and CKF21 of K channels corresponding tothe pixel C1F21 on the training image or its processed feature map 300may be concatenated, . . . , features C1FWH, C2FWH, . . . , and CKFWH ofK channels corresponding to the pixel C1FWH on the training image or itsprocessed feature map 300 may be concatenated, and the like. That is,features of each K channels corresponding to every pixel on the trainingimage or its processed feature map 300 may be concatenated to generatethe reshaped feature map 400A.

Herein, the reshaped feature map 400A may have a width of W, a height ofH·K, and a channel of

${{CEIL}\left( \frac{L}{K} \right)}.$

Also, each channel of the reshaped feature map 400A may correspond toeach group comprised of K channels of the training image or itsprocessed feature map 300. That is, a first channel of the reshapedfeature map 400A may correspond to a first channel to a K-th channel ofthe training image or its processed feature map 300, and a secondchannel of the reshaped feature map 400A may correspond to a (K+1)-thchannel to a (2·K)-th channel of the training image or its processedfeature map 300. Also, a

${{CEIL}\left( \frac{L}{K} \right)} - {th}$

channel of the reshaped feature map 400A may correspond to a

$\left\{ {{\left( {{{CEIL}\ \left( \frac{L}{K} \right)}\  - 1} \right) \cdot K} + 1} \right\} - {th}$

channel to an L-th channel of the training image or its processedfeature map 300.

Then, the learning device 100 may instruct the subsequent convolutionallayer 123 to apply the 1×K convolution operation to the reshaped featuremap 400A, to thereby generate the adjusted feature map 400B whose volumeis adjusted.

As one example, by referring to FIGS. 4A and 4B, a feature of a pixelC1F11′ is generated by applying the 1×K convolution operation to C1F11,C2F11, and CKF11, corresponding to a size of 1×K, of the reshapedfeature map 400A in FIG. 4A, a feature of a pixel C1F22′ is generated byapplying the 1×K convolution operation to C1F22, C2F22, . . . , andCKF22, a feature of a pixel C1FWH′ is generated by applying the 1×Kconvolution operation to C1FWH, C2FWH, . . . , and CKFWH, and the like.Herein, the size of 1×K may be a kernel size of the convolutionallayers. As a result, by applying the 1×K convolution operation to thereshaped feature map 400A in FIG. 4A, the adjusted feature map 400Bhaving a width of W and a height of H as in FIG. 4B may be generated.Therefore, compared to the 1×1 convolution operation on the trainingimage or its processed feature map 300, the amount of computationbecomes 1/K by generating the reshaped feature map 400A and performingthe 1×K convolution operation, and thus the speed of the convolutionoperation is increased by a factor of K. Herein, the number of channelsof the adjusted feature map 400B may correspond to the number ofkernels, i.e., the number of the filters, of the subsequentconvolutional layer 123 performing the 1×K convolution operation. As oneexample, if the number of the kernels of the subsequent convolutionallayer 123 is M, the number of the channels of the adjusted feature map400B may be M.

The method using the 1×K convolution operation is described above,however, features of K channels of the training image or its processedfeature map 300 may be concatenated in a direction of a width and thenthe K×1 convolution operation may be performed.

Then, by referring to FIGS. 3 and 5A, the features C1F11, C2F11, . . . ,and CKF11 of K channels corresponding to the pixel C1F11 on the trainingimage or its processed feature map 300 in FIG. 3 may be concatenated ina direction of a width, the features C1F12, C2F12, and CKF12 of Kchannels corresponding to the pixel C1F12 on the training image or itsprocessed feature map 300 may be concatenated in the direction of thewidth, . . . , the features C1FWH, C2FWH, . . . , and CKFWH of Kchannels corresponding to the pixel C1FWH on the training image or itsprocessed feature map 300 may be concatenated in the direction of thewidth, and the like. That is, the features of each K channelscorresponding to every pixel on the training image or its processedfeature map 300 may be concatenated in the direction of the width togenerate the reshaped feature map 500A.

Herein, the reshaped feature map 500A may have a width of W·K, a heightof H, and a channel of

${{CEIL}\left( \frac{L}{K} \right)}.$

Also, each channel of the reshaped feature map 500A may correspond toeach group comprised of K channels of the training image or itsprocessed feature map 300. That is, a first channel of the reshapedfeature map 500A may correspond to a first channel to a K-th channel ofthe training image or its processed feature map 300, and a secondchannel of the reshaped feature map 500A may correspond to a (K+1)-thchannel to a (2·K)-th channel of the training image or its processedfeature map 300. Also, a

${{CEIL}\left( \frac{L}{K} \right)} - {th}$

channel of the reshaped feature map 500A may correspond to a

$\left\{ {{\left( {{{CEIL}\ \left( \frac{L}{K} \right)}\  - 1} \right) \cdot K} + 1} \right\} - {th}$

channel to an L-th channel of the training image or its processedfeature map 300.

Then, the learning device 100 may instruct the subsequent convolutionallayer 123 to apply the K×1 convolution operation to the reshaped featuremap 500A, to thereby generate the adjusted feature map 500B whose volumeis adjusted.

As one example, by referring to FIGS. 5A and 5B, a feature of a pixelC1F11″ is generated by applying the K×1 convolution operation to C1F11,C2F11, . . . , and CKF11, corresponding to a size of K×1, of thereshaped feature map 500A in FIG. 5A, a feature of a pixel C1F12″ isgenerated by applying the K×1 convolution operation to C1F12, C2F12, . .. , and CKF12, . . . , a feature of a pixel C1FWH″ is generated byapplying the K a pixel C1FC1F11″ is generated by applying the KCKFWH,and the like. Herein, the size of K×1 may be a kernel size of theconvolutional layers. As a result, by applying the K×1 convolutionoperation to the reshaped feature map 500A in FIG. 5A, the adjustedfeature map 500B having a width of W and a height of H as in FIG. 5B maybe generated.

Next, the learning device 100 may instruct a post-processing layer 124to post-process the adjusted feature map 500B outputted from thesubsequent convolutional layer 123. Herein, the post-processing layer124 may include at least one of the convolutional layer, the batchnormalization layer, the activation layer, the pooling layer, and an FClayer, and may generate probability information representing classifiedfeatures of the adjusted feature map 500B or its processed feature mapoutputted from the subsequent convolutional layer 123. However, thescope of the post-processing layer 124 is not limited thereto, that is,the post-processing layer 124 may include each layer which forms the CNNfor image processing.

Then, the learning device 100 may instruct an output layer 125 togenerate at least one output by referring to features on the adjustedfeature map 500B or its processed feature map, and may instruct a losslayer 126 to calculate one or more losses by referring to the output andits corresponding at least one ground truth, to thereby learn at leastpart of parameters of the subsequent convolutional layer 123 bybackpropagating the losses. Herein, the learning device 100 may directlyinput the adjusted feature map 500B into the output layer 125, withoutinstructing the post-processing layer 124 to post-process the adjustedfeature map 500B.

FIG. 6 is a drawing schematically illustrating a testing device of theCNN using the 1×K or the K×1 convolution operation in accordance withone example embodiment of the present disclosure, and by referring toFIG. 6, the testing device 200 may include a communication part 210 anda processor 220.

In addition, the testing device may further include a memory 215 capableof storing computer readable instructions for performing followingprocesses. As one example, the processor, the memory, a medium, etc. maybe integrated with an integrated processor.

First, the communication part 210 may acquire or support another deviceto acquire at least one test image.

Herein, the CNN using the 1×K or the K×1 convolution operation inaccordance with one example of the present disclosure may be assumed tohave been learned by the learning method described by referring to FIGS.2 to 5B.

For reference, in the description below, the phrase “for training” isadded for terms related to the learning processes, and the phrase “fortesting” is added for terms related to testing processes, to avoidpossible confusion.

That is, if at least one training image has been acquired, the learningdevice may have performed processes of (a) instructing the reshapinglayer to two-dimensionally concatenate each of features in each groupcomprised of each corresponding K channels among all channels of thetraining image or its processed feature map, to thereby generate areshaped feature map for training, and instructing the subsequentconvolutional layer to apply the 1×K convolution operation or the K×1convolution operation to the reshaped feature map for training, tothereby generate an adjusted feature map for training whose volume isadjusted. Herein, said processed feature map for training is a featuremap generated by at least one of (i) a method of applying theconvolution operations to the training image, (ii) that of applyingsubsequent operations, e.g., the batch normalization operation, theactivation operation, the pooling operation, to a result of said methodof (i), and (iii) that of applying additional convolution operations toa result of said method of (ii); (b) instructing the output layer togenerate at least one output for training by referring to features onthe adjusted feature map for training or its processed feature map, andinstructing the loss layer to calculate one or more losses by referringto the output for training and its corresponding at least one groundtruth, to thereby learn at least part of parameters of the subsequentconvolutional layer by backpropagating the losses.

Next, the processor 220 may perform processes of instructing thereshaping layer to two-dimensionally concatenate each of features ineach group comprised of each corresponding K channels among all channelsof the test image or its processed feature map, to thereby generate areshaped feature map for testing, and instructing the subsequentconvolutional layer to apply the 1×K convolution operation or the K×1convolution operation to the reshaped feature map for testing, tothereby generate an adjusted feature map for testing whose volume isadjusted. Herein, said processed feature map for testing is a featuremap generated by at least one of (i) a method of applying theconvolution operations to the test image, (ii) that of applyingsubsequent operations, e.g., the batch normalization operation, theactivation operation, the pooling operation, to a result of said methodof (i), and (iii) that of applying additional convolution operations toa result of said method of (ii). Then, the processor 220 may instructthe output layer to generate at least one output for testing byreferring to features on the adjusted feature map for testing or itsprocessed feature map.

Herein, the testing device 200 in accordance with one example embodimentof the present disclosure may be a computing device and may be anydevice with a processor capable of computation. For reference, althoughFIG. 6 shows the single testing device 200, the scope of the presentdisclosure is not limited thereto. For example, the testing device maybe configured as several devices to perform its functions.

A method for testing parameters of the CNN using the 1×K or the K×1convolution operation by using the testing device 200 in accordance withone example embodiment of the present disclosure is described byreferring to FIG. 7 as follows. In the description below, the parteasily deducible from the learning method described by referring toFIGS. 1 to 5B will be omitted.

First, on condition that at least part of parameters of a subsequentconvolutional layer 223 has been learned according to the learningmethod described by referring to FIGS. 1 to 7, if the test image isinputted, the testing device 200 may instruct a pre-processing layer 221to pre-process the test image, to thereby generate the processed featuremap for testing.

Herein, the pre-processing layer 221 may include at least one of theconvolutional layer, the batch normalization layer, the activationlayer, and the pooling layer, and may apply the convolution operationsto the test image, to thereby generate the processed feature map fortesting. Herein, the processed feature map for testing is a feature mapgenerated by at least one of (i) a method of applying the convolutionoperations to the test image, (ii) that of applying subsequentoperations, e.g., the batch normalization operation, the activationoperation, the pooling operation, to a result of said method of (i), and(iii) that of applying additional convolution operations to a result ofsaid method of (ii). However, the scope of the pre-processing layer 221is not limited thereto, that is, the pre-processing layer 221 mayinclude each layer which forms the CNN for image processing.

Next, the testing device 200 may instruct a reshaping layer 222 totwo-dimensionally concatenate each of features in each group comprisedof each corresponding K channels among all channels of the test image orits processed feature map, to thereby generate the reshaped feature mapfor testing. Herein, said processed feature map for testing is a featuremap generated by at least one of (i) a method of applying theconvolution operations to the test image, (ii) that of applyingsubsequent operations, e.g., the batch the normalization operation, theactivation operation, the pooling operation, to a result of said methodof (i), and (iii) that of applying additional convolution operations toa result of said method of (ii). Herein, the testing device 200 maydirectly input the test image into the reshaping layer 222, withoutinstructing the pre-processing layer 221 to pre-process the test image.

Herein, if the number of channels of the test image or its processedfeature map is not a multiple of K, the testing device 200 may instructthe reshaping layer 222 to add at least one dummy channel to thechannels corresponding to each of the pixels such that the number of thechannels including the at least one dummy channel is a multiple of K,and to concatenate said each of features in said each group comprised ofsaid each corresponding K channels among said all channels, includingthe at least one dummy channel, of the test image or its processedfeature map.

That is, supposing that a width of the test image or its processedfeature map is W and a height thereof is H, and that the number ofchannels thereof is L, the testing device 200 may instruct the reshapinglayer 222 to (i) generate the reshaped feature map for testing having awidth of W, a height of H·K, and a channel of

${{CEIL}\left( \frac{L}{K} \right)},$

or (ii) generate the reshaped feature map for testing having a width ofW·K, a height of H, and a channel of

${{CEIL}\left( \frac{L}{K} \right)}.$

Further, if a size of a final part of the reshaped feature map fortesting on a

$\left\{ {{CEIL}\ \left( \frac{L}{K} \right)} \right\} - {th}$

channel is different from a size of the width of W and the height ofH·K, the testing device 200 may instruct the reshaping layer 222 to addat least one zero padding such that the final part of the reshapedfeature map for testing on the

$\left\{ {{CEIL}\ \left( \frac{L}{K} \right)} \right\} - {th}$

channel has the width of W and the height of H·K, or if the size of thefinal part of the reshaped feature map for testing on the

$\left\{ {{CEIL}\ \left( \frac{L}{K} \right)} \right\} - {th}$

channel is different from a size of a width of W·K and a height of H,the testing device 200 may instruct the reshaping layer 222 to add atleast one zero padding such that the final part of the reshaped featuremap for testing on the

$\left\{ {{CEIL}\ \left( \frac{L}{K} \right)} \right\} - {th}$

channel has the width of W·K and the height of H.

Herein, the reshaped feature map for testing may have a width of W, aheight of H·K, and a channel of

${{CEIL}\ \left( \frac{L}{K} \right)}.$

Also, each channel of the reshaped feature map for testing maycorrespond to each group comprised of K channels of the test image orits processed feature map. That is, a first channel of the reshapedfeature map for testing may correspond to a first channel to a K-thchannel of the test image or its processed feature map, and a secondchannel of the reshaped feature map for testing may correspond to a(K+1)-th channel to a (2·K)-th channel of the test image or itsprocessed feature map. Also, a

${{CEIL}\left( \frac{L}{K} \right)} - {th}$

channel of the reshaped feature map for testing may correspond to a

$\left\{ {{\left( {{{CEIL}\left( \frac{L}{K} \right)} - 1} \right) \cdot K} + 1} \right\} - {th}$

channel to an L-th channel of the test image or its processed featuremap.

Then, the testing device 200 may instruct the subsequent convolutionallayer 223 to apply the 1×K convolution operation to the reshaped featuremap for testing, to thereby generate the adjusted feature map fortesting whose volume is adjusted. Herein, the number of channels of theadjusted feature map for testing may correspond to the number of kernelsof the subsequent convolutional layer 223 performing the 1×K convolutionoperation, that is, the number of the filters. As one example, if thenumber of the kernels of the subsequent convolutional layer 223 is M,the number of the channels of the adjusted feature map for testing maybe M.

The method using the 1×K convolution operation is described above,however, features of K channels of the test image or its processedfeature map may be concatenated in a direction of a width and then theK×1 convolution operation may be performed.

That is, as described by referring to FIG. 5A, the reshaped feature mapfor testing may have a width of W·K, a height of H, and a channel of

${{CEIL}\left( \frac{L}{K} \right)}.$

Also, each channel of the reshaped feature map for testing maycorrespond to each group comprised of K channels of the test image orits processed feature map.

Then, the testing device 200 may instruct the subsequent convolutionallayer 223 to apply the K×1 convolution operation to the reshaped featuremap for testing, to thereby generate the adjusted feature map fortesting whose volume is adjusted.

Next, the testing device 200 may instruct a post-processing layer 224 topost-process the adjusted feature map for testing outputted from thesubsequent convolutional layer 223. Herein, the post-processing layer224 may include at least one of the convolutional layer, the batchnormalization layer, the activation layer, the pooling layer, and the FClayer, and may generate probability information representing classifiedfeatures of the adjusted feature map for testing or its processedfeature map outputted from the subsequent convolutional layer 223.However, the scope of the post-processing layer 224 is not limitedthereto, that is, the post-processing layer 224 may include each layerwhich forms the CNN for image processing.

Then, the testing device 200 may instruct the output layer 225 togenerate at least one output for testing by referring to features on theadjusted feature map for testing or its processed feature map.

The present disclosure has an effect of reducing the amount of theconvolution operations of the CNN efficiently by using the 1×K or theK×1 convolution operation.

The present disclosure has another effect of extracting features on animage by the convolution operations since the number of channels of aninputted feature map or the image is increased by a factor of K usingthe 1×K or the K×1 convolution operation.

The method in accordance with the present disclosure may be provided tobe used for hardware optimization which satisfies KPI (key performanceindex).

The embodiments of the present disclosure as explained above can beimplemented in a form of executable program command through a variety ofcomputer means recordable to computer readable media. The computerreadable media may include solely or in combination, program commands,data files, and data structures. The program commands recorded to themedia may be components specially designed for the present disclosure ormay be usable to a skilled human in a field of computer software.Computer readable media include magnetic media such as hard disk, floppydisk, and magnetic tape, optical media such as CD-ROM and DVD,magneto-optical media such as floptical disk and hardware devices suchas ROM, RAM, and flash memory specially designed to store and carry outprogram commands. Program commands include not only a machine languagecode made by a complier but also a high level code that can be used byan interpreter etc., which is executed by a computer. The aforementionedhardware device can work as more than a software module to perform theaction of the present disclosure and they can do the same in theopposite case.

As seen above, the present disclosure has been explained by specificmatters such as detailed components, limited embodiments, and drawings.They have been provided only to help more general understanding of thepresent disclosure. It, however, will be understood by those skilled inthe art that various changes and modification may be made from thedescription without departing from the spirit and scope of thedisclosure as defined in the following claims.

Accordingly, the thought of the present disclosure must not be confinedto the explained embodiments, and the following patent claims as well aseverything including variations equal or equivalent to the patent claimspertain to the category of the thought of the present disclosure.

1. A method for learning parameters of a CNN using a 1×K convolutionoperation or a K×1 convolution operation, comprising steps of: (a)instructing, by a learning device when at least one training image isacquired, wherein a processed feature map of the training image has awidth (W), a height (H), and a depth (L) comprised of a plurality ofchannels, wherein each channel of the plurality of channels has aplurality of features, and wherein each feature of the plurality offeatures corresponds to each pixel of the processed feature map of thetraining image, a reshaping layer to two-dimensionally concatenate eachof features of K different channels corresponding to said each pixel ineach group comprised of each corresponding K different channels amongthe plurality of channels, to thereby generate a reshaped feature map,wherein each pixel of each channel in the reshaped feature mapcorresponds to each of the two-dimensionally concatenated features insaid each group comprised of said each corresponding K differentchannels, and instructing a subsequent convolutional layer to apply the1×K convolution operation or the K×1 convolution operation to thereshaped feature map, to thereby generate an adjusted feature map whosevolume is adjusted; and (b) instructing, by the learning device, anoutput layer to generate at least one output by referring to features onat least one of the adjusted feature map and a processed feature map ofthe adjusted feature map, and instructing, by the learning device, aloss layer to calculate one or more losses by referring to the outputand a corresponding at least one ground truth of the output, to therebylearn at least part of parameters of the subsequent convolutional layerby backpropagating the losses; further comprising, at the step of (a),instructing, by the learning device, when the depth (L) of the processedfeature map of the training image is not a multiple of K, the reshapinglayer to: add at least one dummy channel to the plurality of channels ofthe processed feature map of the training image such that the depth (L)including the plurality of channels and the at least one dummy channelis a multiple of K, and concatenate said each of features in said eachgroup comprised of said each corresponding K channels among said allplurality of channels, including the at least one dummy channel, of theprocessed feature map of the training image, wherein the processedfeature map is reshaped by uniquely rearranging values of pixels,located in K channels, on a single channel, and the values of the pixelsare maintained before and after being reshaped.
 2. (canceled)
 3. Themethod of claim 1, further comprising, at the step of (a), instructing,by the learning device, the reshaping layer to generate the reshapedfeature map having a width of W, a height of H·K, and a depth of${CEIL}\left( \frac{L}{K} \right)$ channels.
 4. The method of claim 3,wherein the number of kernels of the subsequent convolutional layer isM, and further comprising, at the step of (a), instructing, by thelearning device, the subsequent convolutional layer to apply a 1×Kconvolution operation to the reshaped feature map, to thereby generatethe adjusted feature map having a volume of W·H·M, resulting from awidth of W, a height of H, and a depth of M channels.
 5. The method ofclaim 1, further comprising, at the step of (a), instructing, by thelearning device, the reshaping layer to generate the reshaped featuremap having a width of W·K, a height of H, and a depth of${CEIL}\left( \frac{L}{K} \right)$ channels.
 6. The method of claim 5,wherein the number of kernels of the subsequent convolutional layer isM, and further comprising, at the step of (a), instructing, by thelearning device, the subsequent convolutional layer to apply a K×1convolution operation to the reshaped feature map, to thereby generatethe adjusted feature map having a volume of W·H·M, resulting from awidth of W, a height of H, and a depth of M channels.
 7. The method ofclaim 1, further comprising, at the step of (a), instructing, by thelearning device, the reshaping layer to at least one of: (i) generatethe reshaped feature map having a width of W, a height of H·K, and adepth of ${CEIL}\left( \frac{L}{K} \right)$ channels, and (ii) generatethe reshaped feature map having a width of W·K, a height of H, and adepth of ${CEIL}\left( \frac{L}{K} \right)$ channels, and at least oneof: instructing, by the learning device when a size of a final part ofthe reshaped feature map on a$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} - {th}$ channel isdifferent from a size of a width of W and a height of H·K, the reshapinglayer to add at least one row and column of zero padding such that thefinal part of the reshaped feature map on the$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} - {th}$ channelhas the width of W and the height of H·K, and instructing, by thelearning device when the size of the final part of the reshaped featuremap on the $\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} - {th}$channel is different from a size of a width of W·K and a height of H,the reshaping layer to add at least one row and column of zero paddingsuch that the final part of the reshaped feature map on the$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} - {th}$ channelhas the width of W·K and the height of H.
 8. A method for testing a CNNusing a 1×K convolution operation or a K×1 convolution operation,comprising steps of: (a) on condition that a learning device (i) has,upon acquiring at least one training image, wherein a processed featuremap of the training image has a width (W), a height (H), and a depth (L)comprised of a plurality of channels, wherein each channel of theplurality of channels has a plurality of features for training, andwherein each feature of the plurality of features for trainingcorresponds to each pixel of the processed feature map of the trainingimage, instructed a reshaping layer to two-dimensionally concatenateeach of features for training of K different channels corresponding tosaid each pixel in each group comprised of each corresponding Kdifferent channels among the plurality of channels of the processedfeature map of the training image, to thereby generate a reshapedfeature map for training, wherein each pixel of each channel in thereshaped feature map for training corresponds to each of thetwo-dimensionally concatenated features in said each group comprised ofsaid each corresponding K different channels, and has instructed asubsequent convolutional layer to apply the 1×K convolution operation orthe K×1 convolution operation to the reshaped feature map for training,to thereby generate an adjusted feature map for training whose volume isadjusted, and (ii) has instructed an output layer to generate at leastone output for training by referring to features on at least one of theadjusted feature map for training and a processed feature map of theadjusted feature map for training, and has instructed a loss layer tocalculate one or more losses by referring to the output for training anda corresponding at least one ground truth of the output for training, tothereby learn at least part of parameters of the subsequentconvolutional layer by backpropagating the losses; instructing, by atesting device when at least one test image is acquired, wherein atleast one of the test image and a processed feature map of the testimage has the width (W), the height (H), and the depth (L) comprised ofthe plurality of channels, wherein each channel of the plurality ofchannels has a plurality of features for testing, and wherein eachfeature of the plurality of features for testing corresponds to eachpixel of at least one of the test image and the processed feature map ofthe test image, the reshaping layer to two-dimensionally concatenateeach of features for testing of K different channels corresponding tosaid each pixel in each group comprised of each corresponding Kdifferent channels among the plurality of channels of at least one ofthe test image and the processed feature map of the test image, tothereby generate a reshaped feature map for testing, wherein each pixelof each channel in the reshaped feature map for testing corresponds toeach of the two-dimensionally concatenated features in said each groupcomprised of said each corresponding K different channels, andinstructing the subsequent convolutional layer to apply the 1×Kconvolution operation or the K×1 convolution operation to the reshapedfeature map for testing, to thereby generate an adjusted feature map fortesting whose volume is adjusted; and (b) the testing device instructingthe output layer to generate at least one output for testing byreferring to features on the adjusted feature map for testing or itsprocessed feature map; further comprising, at the step of (a),instructing, by the testing device, when the depth (L) of the processedfeature map of the test image is not a multiple of K, the reshapinglayer to: add at least one dummy channel to the plurality of channels ofthe processed feature map of the test image such that the depth (L)including the plurality of channels and the at least one dummy channelis a multiple of K, and concatenate said each of features in said eachgroup comprised of said each corresponding K channels among said allplurality of channels, including the at least one dummy channel, of theprocessed feature map of the test image wherein the processed featuremap is reshaped by uniquely rearranging values of pixels, located in Kchannels, on a single channel, and the values of the pixels aremaintained before and after being reshaped.
 9. (canceled)
 10. The methodof claim 8, further comprising, at the step of (a), instructing, by thetesting device, the reshaping layer to generate the reshaped feature mapfor testing having a width of W, a height of H·K, and a depth of${CEIL}\left( \frac{L}{K} \right)$ channels.
 11. The method of claim10, wherein the number of kernels of the subsequent convolutional layeris M, and further comprising, at the step of (a), instructing, by thetesting device, the subsequent convolutional layer to apply a 1×Kconvolution operation to the reshaped feature map for testing, tothereby generate the adjusted feature map for testing having a volume ofW·H·M, resulting from a width of W, a height of H, and a depth of Mchannels.
 12. The method of claim 8, further comprising, at the step of(a), instructing, by the testing device, the reshaping layer to generatethe reshaped feature map for testing having a width of W·K, a height ofH, and a depth of ${CEIL}\left( \frac{L}{K} \right)$ channels.
 13. Themethod of claim 12, wherein the number of kernels of the subsequentconvolutional layer is M, and further comprising, at the step of (a),instructing, by the testing device, the subsequent convolutional layerto apply a K×1 convolution operation to the reshaped feature map fortesting, to thereby generate the adjusted feature map for testing havinga volume of W·H·M, resulting from a width of W, a height of H, and adepth of M channels.
 14. The method of claim 8, further comprising, atthe step of (a), instructing, by the testing device, the reshaping layerto at least one of: (i) generate the reshaped feature map for testinghaving a width of W, a height of H·K, and a depth of${CEIL}\left( \frac{L}{K} \right)$ channels, and (ii) generate thereshaped feature map for testing having a width of W·K, a height of H,and a depth of ${CEIL}\left( \frac{L}{K} \right)$ channels, and atleast one of: instructing, by the testing device when a size of a finalpart of the reshaped feature map for testing on a$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} - {th}$ channel isdifferent from a size of a width of W and a height of H·K, the reshapinglayer to add at least one row and column of zero padding such that thefinal part of the reshaped feature map for testing on the$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} - {th}$ channelhas the width of W and the height of H·K, and instructing, by thetesting device when the size of the final part of the reshaped featuremap for testing on the$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} - {th}$ channel isdifferent from a size of a width of W·K and a height of H, the reshapinglayer to add at least one row and column of zero padding such that thefinal part of the reshaped feature map for testing on the$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} - {th}$ channelhas the width of W·K and the height of H.
 15. A learning device forlearning parameters of a CNN using a 1×K convolution operation or a K×1convolution operation, comprising: at least one memory that storesinstructions; and at least one processor configured to execute theinstructions to: perform processes of (I) instructing, when at least onetraining image is acquired by the learning device, wherein a processedfeature map of the training image has a width (W), a height (H), and adepth (L) comprised of a plurality of channels, wherein each channel ofthe plurality of channels has a plurality of features, and wherein eachfeature of the plurality of features corresponds to each pixel of theprocessed feature map of the training image, a reshaping layer totwo-dimensionally concatenate each of features of K different channelscorresponding to said each pixel in each group comprised of eachcorresponding K different channels among the plurality of channels, tothereby generate a reshaped feature map, wherein each pixel of eachchannel in the reshaped feature map corresponds to each of thetwo-dimensionally concatenated features in said each group comprised ofsaid each corresponding K different channels, and instructing asubsequent convolutional layer to apply the 1×K convolution operation orthe K×1 convolution operation to the reshaped feature map, to therebygenerate an adjusted feature map whose volume is adjusted, and (II)instructing an output layer to generate at least one output by referringto features on at least one of the adjusted feature map and a processedfeature map of the adjusted feature map, and instructing a loss layer tocalculate one or more losses by referring to the output and acorresponding at least one ground truth of the output, to thereby learnat least part of parameters of the subsequent convolutional layer bybackpropagating the losses; wherein, at the process of (I), theprocessor, when the depth (L) of the pre-processed feature map of thetraining image is not a multiple of K, instructs the reshaping layer to:add at least one dummy channel to the plurality of channels theprocessed feature map of the training image such that the depth (L)including the plurality of channels and the at least one dummy channelis a multiple of K, and concatenate said each of features in said eachgroup comprised of said each corresponding K channels among said allplurality of channels, including the at least one dummy channel, of thepre-processed feature map of the training image; wherein the processedfeature map is reshaped by uniquely rearranging values of pixels,located in K channels, on a single channel, and the values of the pixelsare maintained before and after being reshaped.
 16. (canceled)
 17. Thelearning device of claim 15, wherein at the process of (I), theprocessor instructs the reshaping layer to generate the reshaped featuremap having a width of W, a height of H·K, and a depth of${CEIL}\left( \frac{L}{K} \right)$ channels.
 18. The learning device ofclaim 17, wherein the number of kernels of the subsequent convolutionallayer is M, at the process of (I), the processor instructs thesubsequent convolutional layer to apply a 1×K convolution operation tothe reshaped feature map, to thereby generate the adjusted feature maphaving a volume of W·H·M, resulting from a width of W, a height of H,and a depth of M channels.
 19. The learning device of claim 15, whereinat the process of (I), the processor instructs the reshaping layer togenerate the reshaped feature map having a width of W·K, a height of H,and a depth of ${CEIL}\left( \frac{L}{K} \right)$ channels.
 20. Thelearning device of claim 19, wherein the number of kernels of thesubsequent convolutional layer is M, at the process of (I), theprocessor instructs the subsequent convolutional layer to apply a K×1convolution operation to the reshaped feature map, to thereby generatethe adjusted feature map having a volume of W·H·M, resulting from awidth of W, a height of H, and a depth of M channels.
 21. The learningdevice of claim 15, wherein at the process of (I), the processorinstructs the reshaping layer to at least one of: (i) generate thereshaped feature map having a width of W, a height of H·K, and a depthof ${CEIL}\left( \frac{L}{K} \right)$ channels, and (ii) generate thereshaped feature map having a width of W·K, a height of H, and a depthof ${CEIL}\left( \frac{L}{K} \right)$ channels, and at least one of:wherein, when a size of a final part of the reshaped feature map on a$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} - {th}$ channel isdifferent from a size of a width of W and a height of H·K, the processorinstructs the reshaping layer to add at least one row and column of zeropadding such that the final part of the reshaped feature map on the$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} - {th}$ channelhas the width of W and the height of H·K, and wherein, when the size ofthe final part of the reshaped feature map on the$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} - {th}$ channel isdifferent from a size of a width of W·K and a height of H, the processorinstructs the reshaping layer to add at least one row and column of zeropadding such that the final part of the reshaped feature map on the$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} - {th}$ channelhas the width of W·K and the height of H.
 22. A testing device fortesting a CNN using a 1×K convolution operation or a K×1 convolutionoperation, comprising: at least one memory that stores instructions; andat least one processor, on condition that a learning device (i) has,upon acquiring at least one training image, wherein a processed featuremap of the training image has a width (W), a height (H), and a depth (L)comprised of a plurality of channels, wherein each channel of theplurality of channels has a plurality of features for training, andwherein each feature of the plurality of features for trainingcorresponds to each pixel of the processed feature map of the trainingimage, instructed a reshaping layer to two-dimensionally concatenateeach of features for training of K different channels corresponding tosaid each pixel in each group comprised of each corresponding Kdifferent channels among the plurality of channels of the processedfeature map of the training image, to thereby generate a reshapedfeature map for training, wherein each pixel of each channel in thereshaped feature map for training corresponds to each of thetwo-dimensionally concatenated features in said each group comprised ofsaid each corresponding K different channels, and has instructed asubsequent convolutional layer to apply the 1×K convolution operation orthe K×1 convolution operation to the reshaped feature map for training,to thereby generate an adjusted feature map for training whose volume isadjusted, and (ii) has instructed an output layer to generate at leastone output for training by referring to features on at least one of theadjusted feature map for training and a processed feature map of theadjusted feature map for training, and has instructed a loss layer tocalculate one or more losses by referring to the output for training anda corresponding at least one ground truth of the output for training, tothereby learn at least part of parameters of the subsequentconvolutional layer by backpropagating the losses; configured to executethe instructions to: perform processes of (I) instructing, when at leastone test image is acquired, wherein at least one of the test image and aprocessed feature map of the test image has the width (W), the height(H), and the depth (L) comprised of the plurality of channels, whereineach channel of the plurality of channels has a plurality of featuresfor testing, and wherein each feature of the plurality of features fortesting corresponds to each pixel of at least one of the test image andthe processed feature map of the test image, the reshaping layer totwo-dimensionally concatenate each of features for testing of Kdifferent channels corresponding to said each pixel in each groupcomprised of each corresponding K different channels among the pluralityof channels of at least one of the test image and the processed featuremap of the test image, to thereby generate a reshaped feature map fortesting, wherein each pixel of each channel in the reshaped feature mapfor testing corresponds to each of the two-dimensionally concatenatedfeatures in said each group comprised of said each corresponding Kdifferent channels, and instructing the subsequent convolutional layerto apply the 1×K convolution operation or the K×1 convolution operationto the reshaped feature map for testing, to thereby generate an adjustedfeature map for testing whose volume is adjusted, and (II) instructingthe output layer to generate at least one output for testing byreferring to features on the adjusted feature map for testing or itsprocessed feature map; wherein, at the process of (I), the processor,when the depth (L) of the processed feature map of the test image is nota multiple of K, instructs the reshaping layer to: add at least onedummy channel to the plurality of channels of the processed feature mapof the test image such that the depth (L) including the plurality ofchannels and the at least one dummy channel is a multiple of K, andconcatenate said each of features in said each group comprised of saideach corresponding K channels among said all plurality of channels,including the at least one dummy channel, of the processed feature mapof the test image wherein the processed feature map is reshaped byuniquely rearranging values of pixels, located in K channels, on asingle channel, and the values of the pixels are maintained before andafter being reshaped.
 23. (canceled)
 24. The testing device of claim 22,wherein at the process of (I), the processor instructs the reshapinglayer to generate the reshaped feature map for testing having a width ofW, a height of H·K, and a depth of ${CEIL}\left( \frac{L}{K} \right)$channels.
 25. The testing device of claim 24, wherein the number ofkernels of the subsequent convolutional layer is M, at the process of(I), the processor instructs the subsequent convolutional layer to applya 1×K convolution operation to the reshaped feature map for testing, tothereby generate the adjusted feature map for testing having a volume ofW·H·M, resulting from a width of W, a height of H, and a depth of Mchannels.
 26. The testing device of claim 22, wherein at the process of(I), the processor instructs the reshaping layer to generate thereshaped feature man for testing having a width of W·K, a height of H,and a depth of ${CEIL}\left( \frac{L}{K} \right)$ channels.
 27. Thetesting device of claim 26, wherein the number of kernels of thesubsequent convolutional layer is M, at the process of (I), theprocessor instructs the subsequent convolutional layer to apply a K×1convolution operation to the reshaped feature map for testing, tothereby generate the adjusted feature map for testing having a volume ofW·H·M, resulting from a width of W, a height of H, and a depth of Mchannels.
 28. The testing device of claim 22, wherein at the process of(I), the processor instructs the reshaping layer to at least one of: (i)generate the reshaped feature map for testing having a width of W, aheight of H·K, and a depth of ${CEIL}\left( \frac{L}{K} \right)$channels, and (ii) generate the reshaped feature map for testing havinga width of W·K, a height of H, and a depth of${CEIL}\left( \frac{L}{K} \right)$ channels, and at least one of:wherein, when a size of a final part of the reshaped feature map fortesting on a$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} \text{-}{th}$channel is different from a size of a width of W and a height of H·K,the processor instructs the reshaping layer to add at least one row andcolumn of zero padding such that the final part of the reshaped featuremap for testing on the$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} \text{-}{th}$channel has the width of W and the height of H·K, and wherein, when thesize of the final part of the reshaped feature map for testing on the$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} \text{-}{th}$channel is different from a size of a width of W·K and a height of H,the processor instructs the reshaping layer to add at least one row andcolumn of zero padding such that the final part of the reshaped featuremap for testing on the$\left\{ {{CEIL}\left( \frac{L}{K} \right)} \right\} \text{-}{th}$channel has the width of W·K and the height of H.